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Array processing based on a linear assumption implies degradations in the source bearing estimation when the array is not linear, which is the usual case in real-world problems. Assuming only that the array is planar (but not necessarily quasilinear), we describe a method to include in an adaptive way information about the sensor locations into the array processing, in order to take into account its geometrical shape. This information can be the relative positions of the sensors given by a mechanical device included in the array. The relative sensor positions can also be estimated using a source with a known bearing, a cooperative target for example. Simulation results in a real underwater noise sample show that these two cases lead to important improvement with respect to the classical array processing, based on a linear assumption, provided that the signal-to-noise ratio of the known source is high enough (around 10 dB in our example).

Background Several studies suggest significant relationships between migration and autism spectrum disorder (ASD) but there are discrepant results. Given that no studies to date have included a pathological control group, the specificity of the results in ASD can be questioned. Aims To compare the migration experience (premigration, migratory trip, postmigration) in ASD and non-ASD pathological control groups, and study the relationships between migration and autism severity. Method Parents’ and grandparents’ migrant status was compared in 30 prepubertal boys with ASD and 30 prepubertal boys without ASD but with language disorders, using a questionnaire including Human Development Index (HDI)/Inequality-adjusted Human Development Index (IHDI) of native countries. Autism severity was assessed using the Child Autism Rating Scale, Autism Diagnostic Observation Schedule and Autism Diagnostic Interview-Revised scales. Results The parents’ and grandparents’ migrant status frequency did not differ between ASD and control groups and was not associated with autism severity. The HDI/IHDI values of native countries were significantly lower for parents and grandparents of children with ASD compared with the controls, especially for paternal grandparents. Furthermore, HDI/IDHI levels from the paternal line (father and especially paternal grandparents) were significantly negatively correlated with autism severity, particularly for social interaction impairments. Conclusions In this study, parents’ and/or grandparents’ migrant status did not discriminate ASD and pathological control groups and did not contribute either to autism severity. However, the HDI/IHDI results suggest that social adversity-related stress experienced in native countries, especially by paternal grandparents, is potentially a traumatic experience that may play a role in ASD development. A ‘premigration theory of autism’ is then proposed.

Knowledge of the noise probability density function (PDF) is central in signal detection problems, not only for optimum receiver structures, but also for processing procedures such as power normalization. Unfortunately, the statistical knowledge must be acquired since the classical assumption of a Gaussian noise PDF is often not valid in underwater acoustics. In this article, statistical modeling is studied using a Gaussian–Gaussian mixture (GGM) for three different underwater noise data sets. It is shown that one of them can be adequately described by a Gaussian–Gaussian mixture, one is very close to a Gaussian model and is described by a mixture with a very small perturbating term, whereas the third one seems closer to a nonstationary version of the Middleton class-A model. The first noise sample is also studied with emphasis on the normalization needed in the receiver in order to achieve a constant false alarm probability and on the optimal receiver structure for the detection of a deterministic signal. It is also shown that the classical noise power estimate, calculating the L2 norm of the observation vector, is a good approximation of the square of the maximum likelihood estimator of the noise amplitude for the Gaussian–Gaussian mixture. The notion of noise alone reference is investigated and the performances of normalized test functions using different power estimates are studied. The principal result of our preliminary study is that the use of the receiver associated with the mixture model can lead to improvements with respect to the classical matched filter, this improvement being measured in terms of receiver operating characteristics (ROC) curves.

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